Nano-scale viscometry reveals an inherent mucus defect in cystic fibrosis
Bibliographic record
Abstract
Abstract Abnormally viscous and thick mucus is a hallmark of cystic fibrosis (CF). How the genetic defect causes abnormal mucus in CF remains unanswered and a question of paramount interest. Mucus is produced by hydration of gel-forming mucin macromolecules that are stored in secretory granules prior to release. Current understanding of mucin/mucus structure before and after secretion remains limited and contradictory models exist. Here we used a molecular viscometer and fluorescence lifetime imaging of primary epithelial cells (Normal and CF) to measure nanometer-scale viscosity. We found significantly elevated intraluminal nanoviscosity in a population of CF mucin granules, indicating an intrinsic, pre-secretory, mucin defect. Validation experiments showed that high nanoviscosity in cellular environments is mainly due to the low mobility of water that hydrates macromolecules. Nanoviscosity influences protein conformational dynamics and function. Its elevation along the protein secretory pathway indicates molecular overcrowding and is expected to alter mucin’s post-translational processing, hydration, and mucus rheology after release. The nanoviscosity of extracellular CF mucus was elevated compared to non-CF mucus. Remarkably, it was higher after secretion than in granules, which suggests mucins have a weakly-ordered state in granules and adopt a highly-ordered, nematic crystalline structure extracellularly. This challenges the classical view of mucus as a porous agarose-like gel and suggests an alternative model for mucin organization before and after secretion. Our study also suggests that endoplasmic reticulum stress due to molecular overcrowding contributes to mucus pathogenesis in CF cells. It encourages the development of therapeutics that target pre-secretory mechanisms in CF and other muco-obstructive lung diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".